Assessment of Factors Influencing the Effectiveness of Real Estate Internally Generated Revenue in Local Government Administration in Oyo State, Nigeria

535 Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 535-546 p ISSN: 2635-3342; e ISSN: 2635-3350 Original Research Article Assessment of Factors Influencing the Effectiveness of Real Estate Internally Generated Revenue in Local Government Administration in Oyo State, Nigeria Adedokun, A.R. and Ibrahim-Muhammad, R.A. Department of Estate Management, Lead City University, Ibadan, Nigeria. *adedokun.ade[email protected]du,ng http://doi.org/10.5281/zenodo.18061848 ARTICLE INFORMATION ABSTRACT Article history: Received 04 Oct. 2025 Revised 11 Nov. 2025 Accepted 14 Nov. 2025 Available online 30 Dec. 2025 The study examined the effectiveness of real estate–based Internally Generated Revenue (IGR) sources across six purposively selected Local Government Areas (LGAs) in Oyo State, to evaluate their contribution to fiscal sustainability. Adopting a quantitative research design, 150 structured questionnaires were distributed to officers directly engaged in IGR administration and real estate management, with 137 retrieved (91.3% response rate). Data were analyzed using descriptive and inferential statistics, including mean scores, standard deviations, and factor analysis. Findings showed a welleducated and experienced workforce, with 59.1% of respondents holding bachelor’s degrees and 82.5% demonstrating high awareness of IGR processes. Among twenty-two identified factors, virtual and augmented reality for property sales ranked highest in effectiveness (mean = 4.35), followed by income levels and affordability (mean = 4.15) and automated rent collection (mean = 4.01). The Kaiser-Meyer-Olkin (KMO) value of 0.830 and Bartlett’s test significance at p < 0.001 confirmed data suitability for factor analysis. Six principal components emerged, which are: Regulatory, Fiscal and Sustainability Factors; Market Dynamics and DemandSide Economics; Legal and Institutional Governance; Technological Innovation and Infrastructure; Financial Accessibility and Affordability; and Digitization and Operational Efficiency; accounting cumulatively for 68.17% of total variance. The results highlight the growing role of digital innovation, fiscal reforms, and financial accessibility in strengthening real estate-driven revenue performance. The study concludes that real estate IGR sources, when efficiently managed through technology and institutional collaboration, can significantly enhance local government fiscal independence and sustainable economic development in Oyo State. © 2025 RJEES. All rights reserved. Keywords: Real estate Internally generated revenue Local Government Administration Oyo State Nigeria 1. INTRODUCTION The fiscal viability of local governments in Nigeria increasingly depends on their capacity to mobilize internally generated revenue (IGR) as allocations from federal and state sources continue to decline. 536 A.R. Adedokun and R.A. Ibrahim-Muhammad et al. / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 535-546 IGR, which represents revenues derived from local governments’ constitutionally assigned powers, has been recognized as a key determinant of financial autonomy and service delivery effectiveness (Odoemene, 2020). However, as Adeola (2017) observed in a study on Ibarapa East Local Government of Oyo State, internally generated funds at the local level remain grossly inadequate to meet administrative and developmental needs. Recent national statistics indicate that Nigerian states collectively generated ₦2.43 trillion in IGR in 2023, representing a 26 percent increase over the previous year, a growth that underscores the untapped potential of internal revenue mobilization at the subnational level (National Bureau of Statistics, 2024). Within the IGR framework, real estate–related sources such as land rates, property taxes, tenement rates, and land use charges have emerged as significant yet underutilized fiscal tools. Adama (2018) and Obinna (2019) noted that property taxation plays a strategic role in funding urban infrastructure, particularly in Lagos and Rivers States. Similarly, Famuyiwa (2020) highlighted the potential of land value capture mechanisms and property tax reforms in strengthening municipal infrastructure financing in Lagos State. These findings emphasize that when effectively managed, real estate–based revenues can provide stable, predictable, and equitable fiscal resources for local governments. Despite this potential, several structural and institutional challenges continue to constrain real estate– based IGR generation in Nigeria. Nnamani et.al. (2023) identified weak assessment systems, poor valuation practices, limited administrative capacity, and low taxpayer compliance as persistent barriers. Umenweke (2024) and Egwaikhide (2019) further argued that inefficiencies in property tax collection and compliance enforcement significantly undermine revenue yields at the local level. In addition, inadequate property databases, complex land tenure systems, and overlapping legal frameworks hinder transparency and accountability in real estate revenue administration (Atakpa, Ocheni, & Nwankwo, 2012). In Oyo State, these challenges are compounded by variations in real estate activity and fiscal capacity between urban and peri-urban local governments, making the study of real estate-based IGR particularly relevant. Adeola (2017) observed that some local councils lack revenue-yielding assets and face administrative constraints that impede fiscal sustainability. Understanding the factors that influence the effectiveness of real estate IGR sources across local government areas, ranging from institutional capacity and market dynamics to technological and regulatory frameworks; can therefore provide insights into improving fiscal performance and governance outcomes. This study aims to assess these factors within selected local governments in Oyo State, offering empirical evidence on how real estatedriven IGR mechanisms can enhance local government autonomy and sustainable economic development. 2. METHODOLOGY The study adopted a quantitative research design to assess the factors influencing the effectiveness of real estate-based Internally Generated Revenue (IGR) sources across six purposively selected Local Government Areas (LGAs) in Oyo State; Ibadan North, Ogbomosho North, Akinyele, Oyo East, Ibarapa North, and Orire. These LGAs were chosen to reflect both urban and peri-urban contexts, capturing variations in fiscal capacity and real estate activity. A total of 150 structured questionnaires were distributed to individuals directly involved in IGR administration and real estate management, with 137 successfully retrieved, representing a 91.3% response rate. The instrument used a five-point Likert scale (ranging from Strongly Effective (5) to Strongly Not Effective (1) to evaluate ten revenue sources. Data were analysed using descriptive statistics, frequencies, percentages, mean scores, standard deviations and factor analysis, to interpret respondents’ views and compare the factors influencing the effectiveness of revenue strategies across LGAs. Findings were presented in tables for clarity and comparative insight into real estate-based IGR performance within the study area. 3. RESULTS AND DISCUSSION This section presents and analyses the data obtained from respondents across the six selected Local Government Areas in Oyo State. The analysis highlights the perceived effectiveness of various real estate based Internally Generated Revenue (IGR) sources using descriptive statistical tools. 537 A.R. Adedokun and R.A. Ibrahim-Muhammad et al. / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 535-546 3.1. Socioeconomic Characteristics of Respondents This subsection examines the socioeconomic characteristics of respondents, providing insights into their demographic and professional profiles. These attributes help contextualize the perspectives shared on real estate based Internally Generated Revenue (IGR) generation across the selected Local Government Areas. Table 1 presents socioeconomic characteristics of the respondents, this shows the variables that used in ascertaining the reliability of the information provided for analysis Table 1: Socioeconomic characteristics of respondents involved in internally generated revenue in local government administration Classification Categories Frequency Percent (%) Gender Male 78 56.9 Female 59 43.1 Total 137 100.0 Highest Academic Qualification HND 18 13.1 PGD 27 19.7 BSc/BTech 81 59.1 MSc/MTech 10 7.3 PhD 1 .7 Total 137 100.0 Position in Service Estate Officers 46 33.6 Local Government Revenue Officer 15 10.9 Tax Assessment and Compliance Officer 24 17.5 Legal Officers and Dispute Resolution Officer 28 20.4 Community and Public Relations Officer 18 13.1 Director of Lands 6 4.4 Total 137 100.0 Level of awareness of IGR Very Not Aware 11 8.0 Not Aware 7 5.1 Neutral 6 4.4 Aware 53 38.7 Very Aware 60 43.8 Total 137 100.0 Department/Unit Estate Unit 51 37.2 Revenue Unit 28 20.4 Legal Unit 11 8.0 Community Development Unit 8 5.8 Public Relation Unit 9 6.6 Tax and Compliance Unit 30 21.9 Total 137 100.0 The information presented in Table 1 reveals a modest male predominance among respondents, with 56.9% (78) males and 43.1% (59) females, indicating a fairly balanced gender representation in local government revenue administration. In terms of education, most respondents possess tertiary qualifications: 59.1% hold Bachelor’s degrees, 19.7% have Postgraduate Diplomas, and 13.1% possess HNDs, while 7.3% hold Master’s degrees and only 0.7% a PhD. This shows a generally well-educated workforce, though advanced academic expertise remains limited. Regarding position, Estate Officers form the largest group at 33.6%, followed by Legal Officers and Dispute Resolution Officers (20.4%), Tax Assessment and Compliance Officers (17.5%), Community and Public Relations Officers (13.1%), Local Government Revenue Officers (10.9%), and Directors of Lands (4.4%). This pattern reflects the multidisciplinary nature of IGR operations, with estate management playing a central role. Awareness of IGR processes is notably high, as 82.5% of respondents are either Aware (38.7%) or Very Aware (43.8%), while only 13.1% have limited awareness and 4.4% are neutral, an indication of strong institutional understanding of revenue mechanisms. Across departments, the Estate Unit accounts for 37.2% of respondents, the Tax and Compliance Unit 21.9%, and the Revenue Unit 20.4%. Smaller proportions are from the Legal (8.0%), Public Relations (6.6%), and 538 A.R. Adedokun and R.A. Ibrahim-Muhammad et al. / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 535-546 Community Development (5.8%) units. This distribution underscores the dominance of estate-related activities and taxation in local government internally generated revenue in Oyo State, supported by legal and community engagement functions. 3.2. Factors Influencing the Effectiveness of Real Estate Revenue Generation Strategies in the Study Area To investigate the factors influencing the effectiveness of real estate revenue generation strategies, data was obtained and analyzed based on the following scaled responses to investigate the effectiveness of real estate revenue generation strategies in the study area with the use of likert scaled option; strongly influential (5), influential (4), somewhat influential (3), not influential (2) and strongly not influential (1). Table 2 presents and discuss the various factors influencing the effectiveness of real estate revenue generation strategies in the study area. Showing the general perspective of six (6) various local government used for the survey Table 2: Factors influencing the effectiveness of real estate revenue generation strategies in the study area Factors SI (5) 1 (4) SWI (3) NI (2) SNI (1) Mean Score Std. Deviation Ranking Virtual and augmented reality for property sales 79 33 19 6 0 4.3504 .87943 1st Income levels and affordability 63 47 16 7 4 4.1533 1.01381 2nd Automated rent collection and property management 47 67 7 10 6 4.0146 1.04308 3rd Competition in the market 55 35 30 14 3 3.9124 1.10796 4th Economic growth and stability 39 52 27 15 4 3.7810 1.06905 5th Infrastructure and service availability 54 35 19 20 9 3.7664 1.29059 6th Property taxation policies 50 27 28 26 6 3.6496 1.26950 7th Land use regulations and zoning laws 40 45 20 27 5 3.6423 1.19888 8th Foreign exchange rates 49 25 30 31 2 3.6423 1.22317 9th Market demand and supply 50 34 24 8 21 3.6131 1.42069 10th Ease of land registration and title issuance 35 36 45 20 1 3.6131 1.04493 11th Climate change and disaster risk management 39 44 24 21 9 3.6058 1.23287 12th Building permit and compliance requirements 41 41 19 30 6 3.5912 1.24590 13th Efficient debt and credit management 42 41 18 22 14 3.5474 1.34477 14th Diversification of property portfolio 41 35 24 29 8 3.5255 1.27810 15th Access to mortgage and real estate loans 36 39 24 33 5 3.4964 1.21947 16th Waste management and sanitation standards 41 24 40 26 6 3.4964 1.22549 17th Property location and accessibility 37 36 31 18 15 3.4526 1.31155 18th Inflation and interest rates 37 32 27 35 6 3.4307 1.25321 19th Vacancy rates and tenant turnover 37 27 35 32 6 3.4161 1.23457 20th Land tenure security 30 29 26 27 25 3.0876 1.42186 21st Alternative Dispute Resolution (ADR) for real estate 31 25 24 31 26 3.0292 1.44478 22nd Table 2 presents the findings on the factors influencing real estate operations shows that virtual and augmented reality (VR/AR) ranked highest with a mean of 4.35, underscoring the growing adoption of immersive technologies that enhance property marketing and investor confidence. Income levels and affordability followed closely (mean = 4.15), reflecting the persistent issue of limited housing accessibility among lowand middle-income earners. Automation of rent collection and property management ranked third (mean = 4.01), indicating a strong shift toward digital transformation in estate management. Other key factors of moderate influence include market competition (3.91), economic stability (3.78), infrastructure availability (3.77), and property taxation policies (3.65), all central to investment confidence and market growth. Land use regulations (3.64), foreign exchange rates (3.64), demand-supply dynamics (3.61), and ease of land registration (3.61) also featured prominently, highlighting regulatory and procedural challenges in property transactions. Lower-ranking factors such as building permits (3.59), credit and debt management (3.55), mortgage access (3.50), and waste management standards (3.50) point to institutional and infrastructural limitations that constrain market efficiency. At the bottom of the ranking were vacancy rates 539 A.R. Adedokun and R.A. Ibrahim-Muhammad et al. / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 535-546 (3.42), land tenure security (3.09), and alternative dispute resolution (ADR) mechanisms (3.02), suggesting that tenure clarity and conflict resolution are undervalued despite their importance in sustaining investor and tenant confidence. Table 3 presents the results of the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy and Bartlett’s Test of Sphericity, both of which are preliminary tests to determine the suitability of data for factor analysis. Table 3: KMO and Bartlett's test on factors influencing the effectiveness of real estate revenue generation strategies Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .830 Bartlett's Test of Sphericity Approx. Chi-Square 1493.762 Df 231 Sig. .000 The KMO value of 0.830 indicates a meritorious level of sampling adequacy based on Kaiser’s (1974) classification, where values between 0.80 and 0.89 are considered very good. This suggests that the correlations among the variables are sufficiently high to warrant the use of factor analysis. Essentially, the data has adequate common variance, meaning that the factors influencing real estate revenue generation are likely to share underlying dimensions that can be statistically extracted and interpreted. The Bartlett’s Test of Sphericity further supports this by yielding a high Chi-square value of 1493.762 with 231 degrees of freedom and a p-value of 0.000, which is highly significant (p < 0.05). This indicates that the correlation matrix is not an identity matrix, meaning the variables are indeed correlated and suitable for dimension reduction. If the test had not been significant, it would imply that the variables are unrelated and factor analysis would be inappropriate. Table 4 presents the communalities of 22 variables included in the factor analysis, showing both the initial and extracted values. Communality represents the proportion of each variable's variance that can be explained by the extracted factors. Table 4: Communalities on factors influencing the effectiveness of real estate revenue generation strategies Initial Extraction Market demand and supply 1.000 .647 Inflation and interest rates 1.000 .719 Foreign exchange rates 1.000 .500 Economic growth and stability 1.000 .643 Property taxation policies 1.000 .628 Land use regulations and zoning laws 1.000 .706 Ease of land registration and title issuance 1.000 .714 Building permit and compliance requirements 1.000 .710 Property location and accessibility 1.000 .598 Vacancy rates and tenant turnover 1.000 .672 Competition in the market 1.000 .565 Diversification of property portfolio 1.000 .664 Efficient debt and credit management 1.000 .660 Climate change and disaster risk management 1.000 .789 Waste management and sanitation standards 1.000 .695 Alternative Dispute Resolution (ADR) for real estate 1.000 .700 Land tenure security 1.000 .661 Income levels and affordability 1.000 .822 Automated rent collection and property management 1.000 .808 Virtual and augmented reality for property sales 1.000 .792 Infrastructure and service availability 1.000 .655 Access to mortgage and real estate loans 1.000 .648 540 A.R. Adedokun and R.A. Ibrahim-Muhammad et al. / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 535-546 The initial communalities are all set at 1.000, which is standard in principal component analysis, assuming that each variable's total variance is initially considered. The extraction communalities, however, show how much of the variance in each variable is accounted for by the retained factors after factor extraction. A review of the extraction values reveals that all variables have communalities above the commonly accepted threshold of 0.50, suggesting that all included variables are meaningfully represented in the factor solution. Among the variables, income levels and affordability (.822), automated rent collection and property management (.808), and virtual and augmented reality for property sales (.792) have the highest communalities, indicating that these variables are highly influenced by the underlying latent constructs extracted during the analysis. This reinforces earlier findings in the ranking data that these factors are central to real estate revenue generation. Similarly, climate change and disaster risk management (.789) and ease of land registration and title issuance (.714) also exhibit strong representation, indicating their increasing importance in shaping revenue sustainability and operational efficiency in property markets. On the other hand, variables such as foreign exchange rates (.500) and competition in the market (.565) have lower communalities, though still above the threshold. These suggest that while they contribute to the overall factor structure, they may be influenced by more unique or context-specific factors that are not as strongly aligned with the main extracted components. Table 5 provides a comprehensive summary of the factor extraction process through the principal component analysis (PCA), illustrating the amount of total variance explained by each extracted component. The Table is divided into three key parts: Initial Eigenvalues, Extraction Sums of Squared Loadings, and Rotation Sums of Squared Loadings. Each part presents the total variance explained by the components before and after extraction and rotation. From the initial eigenvalues, it is observed that six components have eigenvalues greater than 1.0, following Kaiser’s criterion for factor retention (Kaiser, 1974). These six components together account for 68.172% of the total variance in the dataset. This percentage exceeds the generally accepted threshold of 60% in social sciences, indicating a robust explanatory model for understanding the underlying factors influencing real estate revenue generation strategies. The first component, with an eigenvalue of 7.533, explains 34.242% of the total variance, indicating that it captures the most significant share of the variability among the observed variables. Subsequent components explain decreasing amounts of variance, with the second (8.334%), third (7.745%), fourth (6.810%), fifth (5.964%), and sixth (5.077%) components cumulatively explaining up to the 68.172% total variance. The extraction sums of squared loadings mirror the initial eigenvalues, confirming that these six components were retained based on their eigenvalues and are statistically justified for further interpretation. The rotation sums of squared loadings, which apply Varimax rotation to simplify the structure and improve interpretability, redistribute the variance more evenly across components. After rotation, the first component accounts for 23.769% of the variance, while the second and third account for 17.201% and 7.384%, respectively. This redistribution helps to ensure that no single factor dominates the solution and enhances the ability to interpret distinct clusters of variables. The rotation results show a cumulative variance of 68.172% across the six retained components, indicating that the factor model remains comprehensive and reliable even after rotation. This supports the use of these factors in subsequent analyses such as naming, interpretation, and regression modeling. In applied research, such a high cumulative variance explained demonstrates a good model fit and suggests that the underlying factors are effectively capturing the complexity of the determinants of revenue generation strategies in real estate. The scree plot in Figure 1 graphically represents the eigenvalues associated with each component derived from the Principal Component Analysis (PCA). It is a visual tool used to determine the optimal number of components to retain by identifying the point where the curve levels off, often referred to as the "elbow" or "inflection point.". From the plot, the steep descent of the curve from Component 1 to Component 2 indicates a significant drop in eigenvalue, with Component 1 having the highest eigenvalue (approximately 7.5), accounting for the most substantial variance in the dataset. The curve begins to flatten after Component 6, indicating diminishing returns in the variance explained by subsequent components. This suggests that most of the meaningful variance is captured within the first six components, beyond which additional components contribute relatively little. This graphical interpretation is consistent with the Kaiser criterion, which recommends retaining components with eigenvalues greater than 1.0. As shown in the previous Table 5, exactly six 541 A.R. Adedokun and R.A. Ibrahim-Muhammad et al. / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 535-546 components meet this threshold. Therefore, the scree plot supports the statistical decision to retain six components for further analysis and interpretation. Table 5: Total variance explained on factors influencing the effectiveness of real estate revenue generation strategies Figure 1: Scree Plot Table 6 presents the results of the rotated component matrix from the Principal Component Analysis (PCA) using Varimax rotation, which clarifies the relationships among variables and identifies six key thematic components that explain the main determinants of real estate revenue performance. 542 A.R. Adedokun and R.A. Ibrahim-Muhammad et al. / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 535-546 Table 6: Rotated component matrix on factors influencing the effectiveness of real estate revenue generation strategies Component 1 2 3 4 5 6 Market demand and supply -.058 .771 .090 .126 .114 .115 Inflation and interest rates .337 .720 .214 -.011 .077 -.187 Foreign exchange rates .324 .548 .022 .295 -.048 .070 Economic growth and stability .721 .288 .189 -.034 -.017 .055 Property taxation policies .783 .059 .048 .050 .018 -.085 Land use regulations and zoning laws .696 .159 .167 -.284 -.093 -.282 Ease of land registration and title issuance .616 .536 .175 .116 -.019 .050 Building permit and compliance requirements .450 .703 -.009 -.057 .083 -.062 Property location and accessibility .259 .720 -.069 -.057 -.068 .014 Vacancy rates and tenant turnover .393 .686 -.124 -.131 .001 -.123 Competition in the market .619 .298 -.141 .178 -.193 -.062 Diversification of property portfolio .743 .305 .061 -.072 .097 .008 Efficient debt and credit management .786 .098 -.115 .024 .002 .136 Climate change and disaster risk management .788 .301 .205 -.003 .145 .122 Waste management and sanitation standards .589 .409 .198 .167 .144 .306 Alternative Dispute Resolution (ADR) for real estate .201 .012 .781 .020 -.050 -.218 Land tenure security .042 .039 .743 -.119 .145 .264 Income levels and affordability .102 .009 -.089 -.149 .882 .063 Automated rent collection and property management .050 -.025 .012 -.040 -.018 .896 Virtual and augmented reality for property sales .044 -.076 -.225 .799 .285 .120 Infrastructure and service availability -.021 .123 .094 .749 -.221 -.146 Access to mortgage and real estate loans -.087 .135 .325 .254 .664 -.106 The first component, Regulatory, Fiscal, and Sustainability Factors (RFSF), captures the influence of government policy, economic stability, and sustainability measures, emphasizing how taxation, debt management, and environmental responsiveness shape long-term revenue outcomes. The second component, Market Dynamics and Demand-Side Economics Construct (MDDEC), reflects the effects of market behavior, location, pricing, and economic cycles on real estate performance. The third component, Legal and Institutional Governance Factor (LIF), highlights the importance of strong legal frameworks, secure property rights, and effective dispute resolution in fostering investor confidence and consistent returns. The fourth component, Technological Innovation and Physical Infrastructure (TIPF), underscores the role of digital tools and infrastructure in enhancing marketing, transaction efficiency, and accessibility. The fifth component, Financial Accessibility and Affordability Dimension (FAAD), focuses on income levels, affordability, and access to mortgage financing, showing how purchasing power and financial inclusion drive investment activity. Finally, the sixth component, Digitization and Operational Efficiency Factor (DOEF), points to the growing importance of automated property management and digital systems in improving efficiency, reducing costs, and ensuring stable revenue flows. Together, these six components offer a comprehensive view of the multidimensional forces shaping real estate revenue, linking regulatory, market, institutional, technological, financial, and operational dynamics into an integrated framework for understanding sector performance. Table 7 presents the Component Transformation Matrix, showing the correlations between the original and Varimax-rotated components from the Principal Component Analysis (PCA). The matrix illustrates how the factor space was reoriented to enhance interpretability while maintaining orthogonality. 543 A.R. Adedokun and R.A. Ibrahim-Muhammad et al. / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 535-546 Table 7: Component transformation matrix on factors influencing the effectiveness of real estate revenue generation strategies Component 1 2 3 4 5 6 RFSF .781 .606 .142 .029 .048 .008 MDDEC -.427 .521 -.111 .656 .311 .080 LIF -.057 -.133 .676 -.161 .668 .224 TIPF .413 -.503 -.315 .460 .205 .473 FAAD .099 -.256 .567 .575 -.348 -.389 DOEF -.154 .162 .299 -.014 -.540 .754 Rotated Component 1 aligns strongly with the original Component 1 (0.781) and moderately with Component 2 (0.606), indicating a blend of both. Component 2 correlates moderately with original Components 2 (0.521) and 4 (0.656), while Component 3 retains its original structure, showing a strong alignment with Component 3 (0.676). Component 5 combines contributions from original Components 3 (0.567) and 4 (0.575), and Component 6 is largely defined by original Component 6 (0.754) with a moderate influence from Component 5 (−0.540). The matrix confirms that Varimax rotation effectively redistributed factor loadings to achieve clearer thematic distinctions, while all correlation values remained below 1.0, indicating that the rotated components retained statistical independence and validity. Discussion of Findings The findings of this study provide critical insights into the factors influencing real estate–related internally generated revenue (IGR) within Oyo State, Nigeria. The results show that respondents were largely drawn from the revenue and estate management cadres of local governments, with over 80 percent indicating a high level of awareness of IGR mechanisms. This respondent profile underscores that the views expressed in the data are grounded in practical experience with property taxation, revenue collection, and estate administration, making the responses credible for interpreting real estate revenue performance at the local government level. The study identified virtual and augmented reality (VR/AR) applications in property marketing as the most influential factor shaping revenue performance. This finding reflects the global rise of digital real estate marketing tools that are transforming the way properties are promoted and sold. Azmi et.al, (2022) found that virtual tours enhance buyer engagement by providing immersive property experiences that reduce information asymmetry and increase purchase intention. Similarly, Hsiao, Wang & Lin (2024) demonstrated that low-immersion virtual reality improves consumer perception of product quality and satisfaction, which can accelerate sales conversion in real estate transactions. In the Nigerian context, Adilieme (2025) reported that digital technologies, including virtual inspection tools, are becoming increasingly integrated into valuation and property marketing practices, leading to improved client engagement and transaction transparency. The alignment of these studies with the present findings indicates that local practitioners recognize digitization as a practical means of enhancing real estate revenue generation. The second major influence identified was income and affordability. This outcome is consistent with extensive literature linking affordability to real estate market performance. The World Bank (2021) and UNHabitat (2021) observed that Nigeria faces a significant housing deficit of over 17 million units, largely due to low household incomes, rising construction costs, and limited access to mortgage financing. The affordability constraint directly limits the pool of potential buyers and investors, thereby influencing both transaction volumes and property-related revenue. The finding that income and affordability strongly shape IGR performance implies that without addressing household purchasing power and financing access, local governments will continue to experience constrained real estate revenue growth. The study also revealed that automated rent collection and property management systems were ranked among the top three influential factors. This is significant because automation improves collection efficiency and reduces revenue leakages. Property management literature emphasizes that digitized payment systems enhance timely remittances and accountability. Hassan (2021) and Nnwachukwu (2024) noted that automated rent collection minimizes arrears and strengthens landlords’ and agencies’ financial planning by ensuring steady cash inflows. These findings affirm that the adoption of technology in property management is not merely an operational enhancement but a strategic tool for revenue stabilization at the local level.